5,752 research outputs found

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

    Get PDF
    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    General government fiscal plan for 2024–2027

    Get PDF
    The purpose of the General Government Fiscal Plan is to support decision-making related to general government finances as well as compliance with the Medium-Term Objective set for the structural budgetary position of general government finances. The plan contains sections related to central government finances, wellbeing services county finances, local government finances, statutory earnings-related pension funds and other social security funds. The Government prepares the General Government Fiscal Plan for the parliamentary term and revises it annually for the following four years by the end of April. The General Government Fiscal Plan also includes Finland’s Stability Programme, and it meets the EU’s requirement for a medium-term fiscal plan. The General Government Fiscal Plan for 2024–2027 does not propose any new policy definitions. It is based on current legislation and takes into account the impact of the decisions previously made by Prime Minister Marin’s Government on the expenditure and revenue levels in the coming years. This General Government Fiscal Plan does not set any budgetary position targets. The first General Government Fiscal Plan of the Government to be appointed after the parliamentary election in spring 2023 will be drawn up in autumn 2023, and this will include a Stability Programme. The General Government Fiscal Plan also includes the central government spending limits decision, but it does not specify a parliamentary term expenditure ceiling

    The Adirondack Chronology

    Get PDF
    The Adirondack Chronology is intended to be a useful resource for researchers and others interested in the Adirondacks and Adirondack history.https://digitalworks.union.edu/arlpublications/1000/thumbnail.jp

    Machine learning for managing structured and semi-structured data

    Get PDF
    As the digitalization of private, commercial, and public sectors advances rapidly, an increasing amount of data is becoming available. In order to gain insights or knowledge from these enormous amounts of raw data, a deep analysis is essential. The immense volume requires highly automated processes with minimal manual interaction. In recent years, machine learning methods have taken on a central role in this task. In addition to the individual data points, their interrelationships often play a decisive role, e.g. whether two patients are related to each other or whether they are treated by the same physician. Hence, relational learning is an important branch of research, which studies how to harness this explicitly available structural information between different data points. Recently, graph neural networks have gained importance. These can be considered an extension of convolutional neural networks from regular grids to general (irregular) graphs. Knowledge graphs play an essential role in representing facts about entities in a machine-readable way. While great efforts are made to store as many facts as possible in these graphs, they often remain incomplete, i.e., true facts are missing. Manual verification and expansion of the graphs is becoming increasingly difficult due to the large volume of data and must therefore be assisted or substituted by automated procedures which predict missing facts. The field of knowledge graph completion can be roughly divided into two categories: Link Prediction and Entity Alignment. In Link Prediction, machine learning models are trained to predict unknown facts between entities based on the known facts. Entity Alignment aims at identifying shared entities between graphs in order to link several such knowledge graphs based on some provided seed alignment pairs. In this thesis, we present important advances in the field of knowledge graph completion. For Entity Alignment, we show how to reduce the number of required seed alignments while maintaining performance by novel active learning techniques. We also discuss the power of textual features and show that graph-neural-network-based methods have difficulties with noisy alignment data. For Link Prediction, we demonstrate how to improve the prediction for unknown entities at training time by exploiting additional metadata on individual statements, often available in modern graphs. Supported with results from a large-scale experimental study, we present an analysis of the effect of individual components of machine learning models, e.g., the interaction function or loss criterion, on the task of link prediction. We also introduce a software library that simplifies the implementation and study of such components and makes them accessible to a wide research community, ranging from relational learning researchers to applied fields, such as life sciences. Finally, we propose a novel metric for evaluating ranking results, as used for both completion tasks. It allows for easier interpretation and comparison, especially in cases with different numbers of ranking candidates, as encountered in the de-facto standard evaluation protocols for both tasks.Mit der rasant fortschreitenden Digitalisierung des privaten, kommerziellen und öffentlichen Sektors werden immer grĂ¶ĂŸere Datenmengen verfĂŒgbar. Um aus diesen enormen Mengen an Rohdaten Erkenntnisse oder Wissen zu gewinnen, ist eine tiefgehende Analyse unerlĂ€sslich. Das immense Volumen erfordert hochautomatisierte Prozesse mit minimaler manueller Interaktion. In den letzten Jahren haben Methoden des maschinellen Lernens eine zentrale Rolle bei dieser Aufgabe eingenommen. Neben den einzelnen Datenpunkten spielen oft auch deren ZusammenhĂ€nge eine entscheidende Rolle, z.B. ob zwei Patienten miteinander verwandt sind oder ob sie vom selben Arzt behandelt werden. Daher ist das relationale Lernen ein wichtiger Forschungszweig, der untersucht, wie diese explizit verfĂŒgbaren strukturellen Informationen zwischen verschiedenen Datenpunkten nutzbar gemacht werden können. In letzter Zeit haben Graph Neural Networks an Bedeutung gewonnen. Diese können als eine Erweiterung von CNNs von regelmĂ€ĂŸigen Gittern auf allgemeine (unregelmĂ€ĂŸige) Graphen betrachtet werden. Wissensgraphen spielen eine wesentliche Rolle bei der Darstellung von Fakten ĂŒber EntitĂ€ten in maschinenlesbaren Form. Obwohl große Anstrengungen unternommen werden, so viele Fakten wie möglich in diesen Graphen zu speichern, bleiben sie oft unvollstĂ€ndig, d. h. es fehlen Fakten. Die manuelle ÜberprĂŒfung und Erweiterung der Graphen wird aufgrund der großen Datenmengen immer schwieriger und muss daher durch automatisierte Verfahren unterstĂŒtzt oder ersetzt werden, die fehlende Fakten vorhersagen. Das Gebiet der WissensgraphenvervollstĂ€ndigung lĂ€sst sich grob in zwei Kategorien einteilen: Link Prediction und Entity Alignment. Bei der Link Prediction werden maschinelle Lernmodelle trainiert, um unbekannte Fakten zwischen EntitĂ€ten auf der Grundlage der bekannten Fakten vorherzusagen. Entity Alignment zielt darauf ab, gemeinsame EntitĂ€ten zwischen Graphen zu identifizieren, um mehrere solcher Wissensgraphen auf der Grundlage einiger vorgegebener Paare zu verknĂŒpfen. In dieser Arbeit stellen wir wichtige Fortschritte auf dem Gebiet der VervollstĂ€ndigung von Wissensgraphen vor. FĂŒr das Entity Alignment zeigen wir, wie die Anzahl der benötigten Paare reduziert werden kann, wĂ€hrend die Leistung durch neuartige aktive Lerntechniken erhalten bleibt. Wir erörtern auch die LeistungsfĂ€higkeit von Textmerkmalen und zeigen, dass auf Graph-Neural-Networks basierende Methoden Schwierigkeiten mit verrauschten Paar-Daten haben. FĂŒr die Link Prediction demonstrieren wir, wie die Vorhersage fĂŒr unbekannte EntitĂ€ten zur Trainingszeit verbessert werden kann, indem zusĂ€tzliche Metadaten zu einzelnen Aussagen genutzt werden, die oft in modernen Graphen verfĂŒgbar sind. GestĂŒtzt auf Ergebnisse einer groß angelegten experimentellen Studie prĂ€sentieren wir eine Analyse der Auswirkungen einzelner Komponenten von Modellen des maschinellen Lernens, z. B. der Interaktionsfunktion oder des Verlustkriteriums, auf die Aufgabe der Link Prediction. Außerdem stellen wir eine Softwarebibliothek vor, die die Implementierung und Untersuchung solcher Komponenten vereinfacht und sie einer breiten Forschungsgemeinschaft zugĂ€nglich macht, die von Forschern im Bereich des relationalen Lernens bis hin zu angewandten Bereichen wie den Biowissenschaften reicht. Schließlich schlagen wir eine neuartige Metrik fĂŒr die Bewertung von Ranking-Ergebnissen vor, wie sie fĂŒr beide Aufgaben verwendet wird. Sie ermöglicht eine einfachere Interpretation und einen leichteren Vergleich, insbesondere in FĂ€llen mit einer unterschiedlichen Anzahl von Kandidaten, wie sie in den de-facto Standardbewertungsprotokollen fĂŒr beide Aufgaben vorkommen

    'Inventions and adventures': the work of the Stevenson engineering firm in Scotland, c. 1830 - c. 1890

    Get PDF
    This thesis examines the work of the nineteenth-century Stevenson civil engineering firm to argue that civil engineering should be approached geographically both because it takes place in and is shaped by particular spaces, but also because the result of such work reshapes space and the relationship between places. Geographers have extensively analysed the ways in which humans have worked to alter environments, but relatively little attention has been paid to engineering as a socially and geographically transformative process, to the technical questions and to the engineering professionals whose work brought about such change. This thesis analyses engineers as social and technical agents of environmental change, rather than viewing their role as the simple implementation of directives developed elsewhere and by others. It combines insights from the history and historical geography of science, environmental history and the history of technology to make a case for the relevance of an historical geography of engineering. The thesis explores these issues through the work of the Stevenson family. The Stevensons were an Edinburgh-based and internationally-renowned firm of engineers who specialised in the construction of coastal infrastructure. The start and end dates of the thesis indicate, broadly, the careers of David and Thomas Stevenson, who jointly managed the family firm under the name D. & T. Stevenson between 1850 and 1886. The empirical basis for this thesis draws upon the detailed analysis of the firm’s archival records: technical publications, project reports, diaries, correspondence, maps, plans and diagrams. The work of the Stevensons—their engineering epistemologies, practices, and professional identities— are examined through four diverse projects undertaken by the firm in the nineteenth century. These projects are: the training of new engineers; surveying and designing improvement works for the rivers Tay and Clyde; the implementation of a coastal sound-based fog signal network; and the failed attempt to expand Wick harbour through the construction of a breakwater. These projects highlight the range of activities undertaken by nineteenth-century engineers and illustrate the ‘making’ of engineers and the work they did by highlighting training and learning, surveying, maintenance, testing, evaluation, repair and the explanation of failure. With reference to these projects and by drawing upon relevant contextual material, the thesis examines the conceptualisation of geographical space and natural forces in engineering, the relationship between science and engineering, the nature of expertise and notions of engineering judgement, and the role of family, legacy and reputation in securing professional credibility and status. This approach challenges older historiographical traditions which portrayed engineers as individual geniuses. The thesis instead understands engineering to be a combination of specialist knowledge and tacit skill and situates engineers within their social and institutional networks of power and authority. In pointing out that some engineering works failed, the thesis challenges the tendency in histories of engineering works to focus on success. It makes the case for an historical geography of engineering as a way of understanding engineering as an activity, a status and as processes which changed human-environment relations

    Internationalisation dynamics in contemporary South American life sciences: the case of zebrafish

    Get PDF
    We tend to assume that science is inherently international. Geographical boundaries are not a matter of concern in science, and when they do – e.g. due to the rise of nationalist or populist movements – they are thought to constitute a threat to the essence of the scientific enterprise; namely, the global mobility of ideas, knowledge and researchers. Quite recently, we also started to consider that research could become ‘more international’ under the assumption that in doing so it becomes better, i.e. more collaborative, innovative, dynamic, and of greater quality. Such a positive conceptualisation of internationalisation, however, rests on interpretations coming almost exclusively from the Global North that systematically ignore power dynamics in scientific practice and that regard scientific internationalisation as an unproblematic transformative process and as a desired outcome. In Science and Technology Studies (STS), social research on model organisms is perhaps the clearest example of the influence of the dominant vision of internationalisation. This body of literature tends to describe model organism science and their research communities as uniform and harmonious international ecosystems governed by a strong collaborative ethos of sharing specimens, knowledge and resources. But beyond these unproblematic descriptions, how does internationalisation actually transform research on life? To what extent do the power dynamics of internationalisation intervene in contemporary practices of knowledge production and diffusion in this field of research? This thesis revisits the dynamics and practices of scientific internationalisation in contemporary science from the perspective of South American life sciences. It takes the zebrafish (Danio rerio), a small tropic freshwater fish, originally from the Ganges region in India and quite popular in pet shops, as a case study of how complex dynamics of internationalisation intervene in science. While zebrafish research has experienced a remarkable growth in recent years at the global scale, in South America its growth has been unprecedented, allowing average laboratories, which often operate with small budgets and with less well-developed science infrastructures, to conduct world-class research. My approach is based on a consideration of internationalisation as a conceptual model of change. I consider internationalisation to be a process essentially marked by tensions in the spatial, cognitive and evaluative dimensions of scientific practice. These tensions, I claim, are not just a key feature of internationalisation, but also aspects of a conceptual opposition that is geared towards explaining how change comes about in science. By studying the dynamics of internationalisation, I seek to understand various transformations of zebrafish research: from its construction as a research artefact to its diffusion across geographical boundaries. My focus on South America, on the other hand, helps me to understand the complexity of such dynamics beyond the lenses of the dominant discourse of internationalisation that prevails in the STS literature on model organisms. I use mixed-methods (i.e. semi-structured interviews, document analysis, bibliometrics and social network analysis) to observe and interpret transformations of internationalisation at different scales and levels. My analysis suggests first, that internationalisation played an important role in the construction of the zebrafish as a model organism and that, in the infrastructures and practices of resource exchange that sustain the scientific value of the organism internationally, dynamics of asymmetry and empowerment problematise the collaborative ethos of this community. Second, I found that collaborative networks – measured through co-authorships – also played an important role in the diffusion of zebrafish as a model organism in South America. However, I did not find a clear indication of international dependency in the diffusion of zebrafish, explained by a geographical concentration of scientific expertise in the zebrafish collaboration network. Rather than exposing peripheral researchers to novel ideas, networks of international collaboration seem to be more related to access to privileged material infrastructures resulting from the social organisation of scientific labour worldwide. Lastly, by examining practices of biological data curation and researchers’ international mobility trajectories, I describe how dynamics of internationalisation shape the notion of research excellence in model organism science. In this case, I found mobility trajectories to play a key role in boosting researchers’ contributions to the community’s database, especially among researchers from peripheral communities like South America. Overall, while these findings show the value of considering internationalisation as a conceptual model of change in science, more research is needed on the intervention of complex dynamics of internationalisation in other cases and fields of research

    Knowledge Transfer for and through the Replication of Organisational Routines in Franchise Systems

    Get PDF
    Routines are dispositions to behave according to established sets of rules that are also repositories of the organisational memory about “how things get done”. Franchise systems are organisational forms which expand through the replication of routines by new units owned by franchisees. Drawing on insights from the literatures on organisational learning, organisational evolution (under generalised Darwinism), and cognitive psychology, this thesis identifies the building blocks for a conceptual explanation of routine replication in franchise systems. It then proposes an original case study of Yázigi, a large Brazilian franchise system of language schools, which is used to develop a novel process model that captures how knowledge is transferred for and through the replication of routines within an expanding franchise system. Four principal lessons are derived. First, when direct knowledge transfer is not available, artefacts, most notably template representations of routines, are essential. Second, intermediaries, as agents of routine compilation who direct participants to template representations, are crucial to the process of routine replication. Third, just as routines are analogues of habits, routine compilation seems to reproduce habit compilation. Finally, existing learning-related habits of thought may work in favour of or against the adoption of new habits in the replication process. This thesis outlines the prescriptive implications of these lessons for franchise practitioners and details opportunities for future research
    • 

    corecore